In the post-genome era, it is critical to organize genes and proteins into functional biological networks. Our long-term goal is to dissect the transcriptional regulatory networks that regulate every biological process in the cell, understand the regulatory mechanisms, and discover fundamental principles that govern the evolution of these networks. The focus of this proposal is to develop new bioinformatics methods for reconstructing the transcriptional network regulating yeast sporulation, which consists of meiosis and spore morphogenesis, by integrating genomic data from different sources. These methods are uniquely appropriate for identifying combinatorial regulation of transcription factors particularly those transient ones and thus revealing the dynamic realization of the underlying network. The proposed research will contribute significantly to our understanding of yeast sporulation and may shed light on understanding meiosis in higher organisms. The bioinformatics methods developed and validated in the proposed study can also serve as a good starting point to develop equivalent tools for higher organisms. The specific hypothesis behind the proposed research is that not all important transcriptional regulators of yeast sporulation have been identified, and many network links that determine regulatory logic are still missing. The specific aims are: 1. develop a new bioinformatics method to accurately construct transcription modules (defined to consist of a TF, its binding site and its target genes) using gene expression, transcription factor binding and sequence motif information; 2. construct transcription modules of budding yeast sporulation using the above algorithm, discover combinatorial regulation and determine transcriptional network of yeast sporulation by assembling these modules; 3. predict transcription modules and network topologies of sporulation in the 6 newly sequenced yeast genomes based on the network of budding yeast.